As insurers become more technologically advanced, truly good data management stands out as the thing that can make or break a digital transformation in terms of both better customer experience and operational efficiency.
Using customer-centric (instead of policy-centric) data management is one of the best ways an insurer can create a solid, new, and future-proof organizational data structure that’ll help them innovate like crazy now, and establish themselves as a customer-focused leader in the future.
Why Go Customer-Centric?
The answer lies in the fabric of every interaction an insurance company has with its clients. Each touchpoint, from policy inquiries to claims and renewals, hinges on the accessibility and accuracy of customer data.
For instance, claims are the moment of truth in a customer’s journey with an insurer, and often make or break the relationship. When the experience is good and easy for the customer, they’ll stay as a happy customer for a long, long time. But if things go wrong, or it takes ages for a claim to close, customers will shop around and find a new insurer when their renewal term is up.
When you’ve got a system that’s focused on swiftly pulling together comprehensive and accurate customer data, you can automate a lot of the claims processing, reduce errors, and send out automated (yet personalized) messages, boosting customer satisfaction.
Further, the general expectation of consumers today is that institutions selling to them are using their data for personalized offers. When this doesn’t happen, the insurer in question can seem out of date, or like they just don’t care enough to take individual data pieces into consideration.
When an institution (insurer, online retailer, social media platform, etc.) stores data in individual customer records rather than dispersed product files, this shifts the paradigm. They move from the old-school the-customer-is-just-a-number setup, to a personalized relationship between two equalized, interested parties. This boosts customer loyalty because they feel seen and heard by those providers, but it also helps streamline the selling and delivery processes for the providers in major cost-saving ways.
The Challenges of Customer-Centricity with Modern Legacy (Policy-Centric) Systems
Traditional and even “modern” legacy insurance systems often segment customer data across various platforms and databases, leading to inefficiencies and inaccuracies.
For example, John Smith might have three different policy types from the same, very large insurer. Instead of having one customer record that showcases all the policies he has with them, there’d be three different policy records with his name attached to each one.
If he moves and updates the address on his home insurance, for example, that may not get reflected on his auto or life policies.
Fragmented systems like this require repeated data entry, increasing the risk of errors and reducing operational efficiency.
And, instead of being able to send John personalized offers that make sense (like a larger life insurance policy to cover the new home he just bought), insurers who use policy-centric technology are stuck simply catching up to make sure all of John’s records are up to date, instead of having real-time data accuracy.
A Customer-Centric Approach: Single Customer Records in EIS CustomerCore
EIS architecture centralizes customer data in CustomerCoreTM, ensuring every piece of information — from basic contact details to complex claim histories — is stored in a single, easily accessible location.
This unified view not only enhances the accuracy of customer data, but also enables insurers to deliver personalized services more effectively. For example, significant life events like marriage or relocation can trigger automated, relevant communications to customers, like adjusting coverage or updating contact or other policy information.
Claims & AI: Two Places Where Customer-Centricity REALLY Matters
Consider a scenario where an insurer uses EISⓇ to handle a customer’s claim.
The integrated data allows the insurer to quickly verify claim details, streamline approvals, and facilitate payment processing. This not only enhances the customer’s experience during a stressful time, but also reduces the operational load on the insurer’s staff, allowing them to focus on more complex tasks or customer interactions.
Furthermore, a lot of insurers are now looking for ways to intelligently leverage AI and machine learning.
If an insurer has customer records that are clean, streamlined, and up-to-date, AI and machine learning can work wonders. Clean, up-to-date data allows these tools to predict customer needs and behaviors, and to offer tailored products and services that meet nuanced demands.
But if the data on a customer is disjointed and out of date, running AI and machine learning programs that enable communications to customers or seek out operational efficiencies can be a disaster. The algorithm’s predictive capabilities will be out of whack, “personalized” communications could be totally off base or out of date, and training algorithms on old data will just create more inefficiencies that’ll be a pain to deal with long-term.
How EIS CustomerCore Gets the Devil Out of Your Data & Provides Better Outcomes for Everyone
By placing the customer at the heart of their ecosystems, ambitious, future-focused insurers can unlock unprecedented levels of personalization and efficiency, setting new standards in customer care and operational excellence.
EIS CustomerCore does just this, and helps insurers make sure their customer records are always accurate, up-to-date, and not accidentally duplicated, causing confusion. Our data management architecture is designed to append all relevant data to customer records, so insurers, brokers, and the customers themselves can save time and resources at every step of the insurance value chain (pricing, distribution, underwriting, billing, claims, etc).
To see more about CustomerCore architecture and how EIS enables better, data-driven insurance processes and customer experiences, check out our CustomerCore Overview.
